a16z Podcast
a16z’s Lisha Li sits down with Daniel Litt, Assistant Professor of Mathematics at the University of Toronto, to unpack AI’s rapid progress in mathematics, what today’s frontier models can actually do, and what they’re still missing about the way mathematicians think.
Daniel explains why some recent AI-generated results are genuinely impressive, including an autonomous solution to the Erdős unit distance problem, but argues that solving problems is only one part of mathematics. Today’s models can grind through calculations, combine known techniques, and search enormous spaces, but still struggle with intuition, theory building, identifying the right questions, and developing the kind of big-picture understanding that drives much of mathematical progress.
Lisha and Daniel also explore how AI is already changing mathematical research, why an explosion of AI-generated papers could distort academic incentives, and what happens if researchers outsource the work of thinking rather than use AI to deepen it. Ultimately, they ask a question that extends far beyond mathematics: as AI gets better at intellectual work, how do we make sure humans keep getting better at thinking too?
Resources:
Follow Daniel Litt on X: https://x.com/littmath
Follow Lisha Li on X: https://x.com/lishali88
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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